Journal of Emerging Trends in Blockchain Technology

The rapid growth of electronic health records and digital healthcare systems has generated vast amounts of patient data, creating opportunities for data-driven clinical decision-making. This study investigates the effectiveness of machine learning-based predictive analytics in identifying patients at risk of chronic diseases at an early stage. A retrospective dataset comprising 50,000 anonymized patient records was analyzed using supervised learning algorithms, including logistic regression, random forests, and gradient boosting techniques. The proposed framework integrates demographic information, clinical indicators, lifestyle factors, and historical medical records to develop predictive models for disease risk assessment. Performance evaluation was conducted using accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). Experimental results demonstrated that the gradient boosting model achieved the highest predictive performance, with an AUC-ROC score of 0.92 and an overall accuracy of 89.4%. The findings suggest that machine learning models can significantly improve early disease detection and support healthcare professionals in making timely interventions. The study highlights the potential of predictive analytics to reduce healthcare costs, optimize resource allocation, and enhance patient outcomes while addressing challenges related to data privacy, model interpretability, and ethical considerations.

Modern supply chains involve multiple stakeholders, including manufacturers, suppliers, distributors, logistics providers, and retailers. The increasing complexity of these networks often results in limited visibility, data inconsistencies, and challenges in product traceability. This study proposes a blockchain-based framework for supply chain management that enhances transparency, security, and operational efficiency through decentralized record-keeping. The proposed system integrates blockchain technology with Internet of Things (IoT) sensors and smart contracts to enable real-time monitoring and automated verification of supply chain events. Every transaction and product movement is recorded on a distributed ledger, ensuring data immutability and reducing the risk of fraud, counterfeiting, and unauthorized modifications. A prototype implementation was evaluated using a pharmaceutical supply chain scenario involving multiple stakeholders. Performance analysis demonstrated a 35% reduction in product verification time, a 27% improvement in inventory visibility, and a significant decrease in administrative overhead compared with traditional centralized systems. The findings indicate that blockchain technology can transform supply chain operations by improving trust, accountability, and end-to-end traceability. However, challenges related to scalability, interoperability, regulatory compliance, and implementation costs must be addressed to enable widespread adoption.

Indian stock market, predictive analytics, blockchain technology, machine learning, LSTM, XGBoost, Random Forest, stock price forecasting, data integrity, data security, transparency, smart contracts, IPFS, decentralized storage, cryptographic hashing, hybrid model, financial technology, FinTech, regulatory compliance, data transparency, auditability, forecasting accuracy